发表机构
Tsinghua University; The Hong Kong University of Science and Technology（清华大学; 香港科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究视频间运动转移，此前方法依赖骨骼结构有局限。提出免训练的Motion4Motion框架，对角色运动流建模，突破跨物种运动转移难题，实验结果显示该方法优于基线。
AI 中文摘要
本研究探索视频间的运动转移，这对动画中多样角色至关重要。此前视频运动转移多在人与类人角色间，存在局限，相关技术管道依赖预定义人体骨骼结构，需骨骼条件模型训练，难以推广到不同物种角色，且多样骨骼标注数据有限。本文跳出基于骨骼的运动转移框架，提出免训练的Motion4Motion框架，它对视频中角色运动流建模而非骨骼，使跨物种运动转移更易。大量实验结果和新应用表明该方法显著优于基线。
英文摘要
This work explores the motion transfer from one video to another, which is crucial in animation for diverse characters. Previously, video motion transfer has been largely explored between human and human-like characters, enabling a lot of applications in digital creation. However, these approaches encounter a main limitation. Specifically, related technical pipelines heavily rely on a predefined human skeleton structure and accordingly require skeleton-conditional model training. On the one hand, these methods are difficult to generalize to diverse characters, such as animals from different species, while preserving their unique motion styles. On the other hand, labeled data in diverse skeletons is limited, which additionally restricts the large-scale training for the task. In this paper, we jump out of the skeleton-based motion transfer framework and propose a training-free motion transfer framework, named Motion4Motion. Motion4Motionmodels the motion flow of the character in a video instead of skeletons, which makes motion transfer across species easier. Extensive experimental results and novel applications show our methods outperform baselines impressively. Project page is available at https://lhchen.top/Motion4Motion.
CommentsSIGGRAPH 2026
